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Data warehouse ⇄ Human resources

BigQuery to Greenhouse integration — real-time, two-way sync

Keep BigQuery and Greenhouse in sync without custom scripts. Cut weeks of integration work, eliminate silent data drift, and give your team a single, reliable source of truth.

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Why teams connect BigQuery and Greenhouse

Land the people and organization records from Greenhouse in BigQuery as live tables for workforce reporting, without extract jobs, and write computed results back where Greenhouse can use them.

Workforce data is some of the most requested data in the warehouse and some of the most awkward to move: the record types are many, the fields carry sensitive personal information, the APIs are strict, and hand-built extract jobs go stale or break quietly. Whether Greenhouse is the system of record for employees and payroll, for candidates and applications, or for learners and course completions, the reporting belongs in BigQuery next to everything else the company measures.

Stacksync syncs Scheduled Interviews, Users, Departments and Offices, Candidates from Greenhouse into tables in BigQuery continuously, handling API limits and schema drift as they come. The connection is bi-directional, so values computed in BigQuery, such as headcount rollups, cost allocations, or attrition risk flags, can be written back to fields in Greenhouse where the HR team acts on them. You configure which records and fields cross over, and in which direction, instead of maintaining pipeline code.

Common use cases

  • 01 Feed ML feature tables in BigQuery from operational systems on a continuous schedule
  • 02 Land CRM and ERP records in BigQuery continuously so dashboards reflect business systems without nightly batch jobs
  • 03 Export Scorecards and Scheduled Interviews to a data warehouse to analyze interviewer load and interview outcomes.
  • 04 Two-way sync Candidates and Applications with Postgres so recruiting-ops apps read and update stage, status, and custom fields in SQL while recruiters stay in Greenhouse.

Common sync patterns

Queryable history for planning and audit

A continuously synced copy in BigQuery gives you a durable, queryable record of how Greenhouse's records change over time, for headcount planning and audit questions.

Write-back of computed values

Segments, rollups, or risk flags computed in BigQuery sync back onto the matching records in Greenhouse, where the HR team sees them in the system they already use.

HR data in the warehouse, minus the pipeline

People and organization records from Greenhouse arrive in BigQuery as queryable tables, current within seconds instead of a day behind, replacing hand-built extract jobs.

What you can sync between BigQuery and Greenhouse

Representative objects on each side — any object or custom field can map to any target. Schemas are auto-detected; types are converted between the two systems.

BigQuery objects Greenhouse objects How this pairing syncs
Projects Connection scope: the service account grants access per project. Scheduled Interviews Interview events with interviewers, times, and rooms; full create/update/delete support for calendar and reporting syncs. Projects is specific to BigQuery and Scheduled Interviews to Greenhouse — each maps to any object or custom field on the other side.
Tables The syncable unit: only tables can be synced per the Stacksync docs. Users Greenhouse users (recruiters, hiring managers); read and written, and referenced by the On-Behalf-Of header on every write. Tables is specific to BigQuery and Users to Greenhouse — each maps to any object or custom field on the other side.
Partitioned tables Synced like regular tables; partition columns map to target fields. Departments and Offices Org structure attached to Jobs; read and written to keep reporting hierarchies aligned with an HRIS or warehouse. Partitioned tables is specific to BigQuery and Departments and Offices to Greenhouse — each maps to any object or custom field on the other side.
Clustered tables Supported; clustering is transparent to the sync. Candidates Person records with contact details, tags, and custom fields; read and written via Harvest, often created from sourcing or enrichment pipelines. Clustered tables is specific to BigQuery and Candidates to Greenhouse — each maps to any object or custom field on the other side.
Datasets Organizational container — you pick which dataset’s tables to sync. Applications Links a Candidate to a Job; carries current stage, status, and source. Movable, rejectable, and hireable through Harvest write calls. Datasets is specific to BigQuery and Applications to Greenhouse — each maps to any object or custom field on the other side.

How changes propagate between BigQuery and Greenhouse

Each direction of the sync is driven by what the source system can signal and what the destination accepts — detection, delivery, and expected latency below.

BigQuery Greenhouse Sub-second propagation

DetectionChanges in BigQuery are captured at the source via change data capture — no polling loop against its API. Real-time notification service deployed into your Google Cloud project: Eventarc ("a notification service that enables real-time updates to happen").

DeliveryEach detected change is written to Greenhouse through its API, with automatic retries and rate-limit backoff.

Greenhouse BigQuery Sub-second propagation

DetectionGreenhouse notifies Stacksync of record changes through webhook events. HMAC-SHA256 signed webhooks for candidate, application, job, and interview events, plus polling list endpoints with created_after / updated_after /.

DeliveryEach detected change is applied to BigQuery as a row-level write, with types converted between the two schemas.

Rate-limit considerations

  • BigQuery: Subject to Google Cloud quotas on queries, DML, and streaming; DML is supported but the platform favors append-heavy batch and streaming loads over row-at-a-time writes.
  • Greenhouse: Harvest enforces a per-integration limit over a rolling 10-second window (X-RateLimit-Limit, commonly 50 requests / 10s for approved integrations); responses carry X-RateLimit-Remaining and, on a 429, X-RateLimit-Reset and Retry-After.
What ships with BigQuery ⇄ Greenhouse

Connect BigQuery and Greenhouse for flexible, real-time data sync.

Real-time sync, workflow automation, event queues, EDI, and monitoring, for every BigQuery–Greenhouse connection.

Real-time

Two-way sync

Changes in BigQuery or Greenhouse instantly reflect in both systems. No stale data, no manual imports.

No-code + pro-code

Workflow automation

Trigger automated workflows whenever BigQuery or Greenhouse data changes, update records, fire webhooks, or kick off sequences without brittle API scripts.

At scale

Event queues

Handle millions of events per minute without losing a single BigQuery or Greenhouse record.

Observability

Monitoring

Track your BigQuery ⇄ Greenhouse sync health, view errors, and replay failed events in one click.

Trading partners

EDI

Transform legacy EDI complexity into simple database interactions between BigQuery and Greenhouse.

How the BigQuery and Greenhouse connectors work

BigQuery

Integration surface
GoogleSQL via the BigQuery REST API, client libraries, JDBC/ODBC drivers, and the Storage Read/Write APIs
Authentication
Google Cloud service account: create a dedicated service account, grant roles (BigQuery Data Editor, BigQuery Job User, Cloud Functions Service Agent, Cloud Run Developer, Eventarc Event Receiver
Change detection
Real-time notification service deployed into your Google Cloud project: Eventarc ("a notification service that enables real-time updates to happen") with a Cloud Run "secure portal for real-time notification service in
Capabilities
read · write · CDC
Rate limits
Subject to Google Cloud quotas on queries, DML, and streaming; DML is supported but the platform favors append-heavy batch and streaming loads over row-at-a-time writes
BigQuery setup guide

Greenhouse

Integration surface
Harvest REST API (plus read-only Job Board API and the Ingestion API for bulk candidate import)
Authentication
HTTP Basic Auth with a Harvest API key (key as username, blank password, colon appended then Base64-encoded); write calls require an On-Behalf-Of header naming the Greenhouse user
Change detection
HMAC-SHA256 signed webhooks for candidate, application, job, and interview events, plus polling list endpoints with created_after / updated_after / last_activity_after filters
Capabilities
read · write · webhooks
Rate limits
Harvest enforces a per-integration limit over a rolling 10-second window (X-RateLimit-Limit, commonly 50 requests / 10s for approved integrations); responses carry X-RateLimit-Remaining and, on a 429, X-RateLimit-Reset and Retry-After.
Greenhouse setup guide
How it works

How to connect BigQuery to Greenhouse — three steps, no code

Configure and sync within minutes, no code. Whether you sync 50k or 100M+ records, Stacksync handles the queues, infra, and plumbing. Integrations are non-invasive and need zero setup on your systems.

  1. 01

    Connect your apps

    Authenticate BigQuery and Greenhouse with each platform's native method — OAuth, API keys, or service accounts — plus secure options like SSH tunneling, IP whitelisting, and VPC peering.

    • OAuth 2.0
    • SSH tunnel
    • VPC peering
    BigQuery connected
    Greenhouse connected
    OAuth 2.0
    SSH tunnel
    SSL certificate
    VPC peering
  2. 02

    Choose tables

    Pick the BigQuery and Greenhouse objects to sync — Stacksync auto-detects both schemas, including custom fields where the platform exposes them. Sync to existing tables, or let Stacksync create new ones with ideal data types.

    • Standard objects
    • Custom objects
    • Auto-schema
    objects · BigQuery ⇄ Greenhouse
    Customers 12,480
    Sales Orders 8,213
    Invoices 5,902
    Items 1,344
  3. 03

    Map fields

    Fields map automatically even when names and types differ. Stacksync handles transformation and type casting for you, zero configuration required.

    • Auto-map
    • Type casting
    • Transforms
    BigQuery Greenhouse
    Company company_name text
    Email email text
    Amount amount numeric
    Created created_at timestamp
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FAQ

BigQuery and Greenhouse integration FAQ

SECURITY

Security teams trust Stacksync

As a data company, we understand the importance of keeping your data secure. Stacksync is built with security best practices to keep your data safe at every layer, and is DPF-certified for US, EU, UK and CH data transfers.

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SSO & SCIM

Let your users access Stacksync from your centralized user management systems. Works with Okta, Azure, Google SSO and more.

Alerts

Immediately get alerted about record syncing issues over email, Slack, PagerDuty and WhatsApp. Resolve issues from a centralized dashboard with retry and revert options.

Secure connection options

Securely connects to your systems with:

OAuth 2 SSH Tunnelling SSL certificates IP Whitelisting VPN gateway VPC peering and more

Related integrations

Every pair below is a real-time, two-way sync. Search all 542 integrations available for BigQuery and Greenhouse.

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